The quality, naturalness, and intelligibility of generated speech by end-to-end neural text-to-speech (TTS) synthesis models have been greatly improved in the past few years [1]}, [2]}, [3]}.
Nowadays, it is in high demand to adapt an existing TTS model to support personalized and multi-prosody speech synthesis for a new target speaker using his/her limited amount of data (e.g., 5 utterances).
We refer to this task as few-shot style transfer for voice cloning in TTS, aiming at transferring speaking style of an arbitrary source speaker to a target speaker's voice under data-scarcity constraint.
In real-world applications, it is promising to endow a TTS model with the capability of conducting fast adaptation, meaning that the users can obtain instantaneous personalized speech interface.
The task of fast few-shot style transfer is very challenging in the sense that the learning algorithm needs to deal with not only a few-shot voice cloning problem (i.e., cloning a new voice using few samples) but also a speaker-prosody disentanglement and control problem.
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